Arguments

If TRUE, level is set to seq(51,99,by=3). This is suitable for
fan plots.

simulate

If TRUE, prediction intervals are produced by simulation rather
than using analytic formulae. Errors are assumed to be normally distributed.

bootstrap

If TRUE, then prediction intervals are produced by simulation using
resampled errors (rather than normally distributed errors).

npaths

Number of sample paths used in computing simulated prediction
intervals.

PI

If TRUE, prediction intervals are produced, otherwise only point
forecasts are calculated. If PI is FALSE, then level,
fan, simulate, bootstrap and npaths are all
ignored.

lambda

Box-Cox transformation parameter. If lambda="auto",
then a transformation is automatically selected using BoxCox.lambda.
The transformation is ignored if NULL. Otherwise,
data transformed before model is estimated.

biasadj

Use adjusted back-transformed mean for Box-Cox
transformations. If transformed data is used to produce forecasts and fitted values,
a regular back transformation will result in median forecasts. If biasadj is TRUE,
an adjustment will be made to produce mean forecasts and fitted values.

...

Other arguments.

Value

An object of class "forecast".

The function summary is used to obtain and print a summary of the
results, while the function plot produces a plot of the forecasts and
prediction intervals.

The generic accessor functions fitted.values and residuals
extract useful features of the value returned by forecast.ets.

An object of class "forecast" is a list containing at least the
following elements:

model

A list containing information about the
fitted model

method

The name of the forecasting method as a
character string

mean

Point forecasts as a time series

lower

Lower limits for prediction intervals

upper

Upper
limits for prediction intervals

level

The confidence values
associated with the prediction intervals

x

The original time series
(either object itself or the time series used to create the model
stored as object).

residuals

Residuals from the fitted model.
For models with additive errors, the residuals are x - fitted values. For
models with multiplicative errors, the residuals are equal to x /(fitted
values) - 1.